L. Devroye

Publications442
h-index63
Citations21,205
Highly Influential Citations2,040
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A Probabilistic Theory of Pattern Recognition
TLDR
The Bayes Error and Vapnik-Chervonenkis theory are applied as guide for empirical classifier selection on the basis of explicit specification and explicit enforcement of the maximum likelihood principle.
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Non-Uniform Random Variate Generation
  • L. Devroye
  • Computer Science, Mathematics
  • 16 April 1986
TLDR
This chapter reviews the main methods for generating random variables, vectors and processes in non-uniform random variate generation, and provides information on the expected time complexity of various algorithms before addressing modern topics such as indirectly specified distributions, random processes, and Markov chain methods.
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Combinatorial methods in density estimation
TLDR
A comparison of the Kernel Estimate and the Vapnik-Chervonenkis Dimension and Covering Numbers shows that the former is significantly more accurate than the latter and the latter is significantly less accurate.
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A Course in Density Estimation
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Consistency of Random Forests and Other Averaging Classifiers
TLDR
A number of theorems are given that establish the universal consistency of averaging rules, and it is shown that some popular classifiers, including one suggested by Breiman, are not universally consistent.
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Nonparametric Density Estimation
TLDR
This chapter describes the background material related to the nonparametric density estimation, taking into account only the univariate case; extending the results to cover more than one variable, however, is often a straightforward task.
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A note on the height of binary search trees
TLDR
It is proved that S is the saturation level of the same tree, that is, the number of full levels in the tree, and H is the height of a binary search tree constructed by standard insertions from a random permutation.
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Lectures on the Nearest Neighbor Method
TLDR
A wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning, is presented in one self-contained volume.
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On the Almost Everywhere Convergence of Nonparametric Regression Function Estimates
Let (X, Y), (X1, Y1), .•, (X,, Yn ) be independent identically distributed random vectors from R dxR, and let E(I Y 1 p) < oo for some p > 1 . We wish to estimate the regression function m(x) = E(Y I…
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Nonparametric density estimation : the L[1] view
Differentiation of Integrals Consistency Lower Bounds for Rates of Convergence Rates of Convergence in L1 The Automatic Kernel Estimate: L1 and Pointwise Convergence Estimates Related to the Kernel…
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